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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Formate-Induced Dissolution and Reprecipitation of a Copper Electrocatalyst during Electrochemical CO 2 Reduction Reaction

Catalyst size, morphology, and crystal structure play crucial roles in determining the activity and selectivity of electrochemical CO 2 reduction reactions, which are known to change during the reaction process. A comprehensive understanding of how, when, and why these parameters evolve under operational conditions is essential for developing stable, efficient, and selective catalysts. In this study, we reveal that formate, one of the reaction products, contributes to the degradation of copper catalysts through a ligand-assisted dissolution mechanism. Utilizing in situ electrochemical atomic force microscopy and ex-situ scanning and transmission electron microscopies, we observed a significant reduction in the size of copper nanoparticles, which decreased from over 30 nm to less than 10 nm in diameter within 60 min of CO 2 RR. The temporal production of formate correlated with the particle size changes. Furthermore, analysis of the electrolyte using inductively coupled plasma optical emission spectroscopy confirmed the dissolution of copper nanoparticles. Control experiments involving various reaction products (H 2 , CO, and HCOO – ) demonstrated that formate significantly promotes copper dissolution, thereby highlighting its role in the ligand-assisted dissolution mechanism of copper electrocatalysts. In conclusion, our findings provide critical insights into copper catalyst behavior during electrochemical CO 2 reduction, facilitating the design of more resilient and effective electrocatalysts.

Catalysts↗
DOE OSTI2025

Twists and turns: stacking and structure-dependent optical response in MoS 2 nanoscrolls

Transition metal dichalcogenide (TMD) nanoscrolls (NSs), specifically MoS 2 NSs, present unique structural and optical properties, exhibiting prominent photoluminescence (PL) signals despite their multilayer nature. Here, this study investigates the structural and spectroscopic characteristics of MoS 2 NSs, correlating them to show the effects of reduced interlayer interactions on excitons of MoS 2 NSs. The reduction in interlayer interaction arises from two main factors: (1) symmetry-broken mixed stacking due to misalignment between layers and (2) a highly inhomogeneous strain profile generated by the Archimedean spiral geometry with positive eccentricity. Transmission electron microscopy, field emission scanning electron microscopy, atomic force microscopy, Raman spectroscopy, and second harmonic generation measurements confirm these findings. Low-temperature PL spectroscopy explores the impact of reduced interlayer interactions on exciton properties such as exciton–phonon coupling and oscillator strength. This study provides crucial insights into the structure, stacking and unique optical properties of TMD NSs, advancing the understanding of interlayer interactions and their impacts in complex quasi-one-dimensional nanostructures.

25 ENERGY STORAGE↗
DOE OSTI2023

SPRUCE Sphagnum Phytobiome Responses to Whole Ecosystem Warming and Elevated Atmospheric CO2 in July, 2017-2021

This dataset reports the carbon (C) and nitrogen (N) isotopic composition of Sphagnum moss tissue collected from the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experimental site within the Marcell Experimental Forest in northern Minnesota, USA. Tissues were collected inside the SPRUCE experimental study plot, where air and peat warming are combined in whole-ecosystem warming treatments, from 2019-2021. Additionally, rates of nitrogen fixation and methane oxidation measured for Sphagnum from 2017, 2019, and 2021. Sphagnum tissue % water content was measured at all timepoints. This dataset was used to conduct analyses within Petro et al (2023). Sphagnum C and N concentrations and 13C and 15N isotopic natural abundance were determined for three replicate Sphagnum tissue samples collected from inside the SPRUCE enclosures in July 2019-2021. Elemental and isotope analyses were conducted at the University of Georgia – Center for Applied Isotope Studies (CAIS; https://cais.uga.edu/). Plant elemental analysis was performed by the micro-Dumas method, while isotopic natural abundance was measured by isotope ratio mass spectrometry. 13C natural abundance is expressed as the per mille (‰) deviation from the Pee Dee Belemnite standard (PDB) 13C:12C ratio (δ13C), while 15N natural abundance is expressed as the ‰ deviation from the N2 atmospheric 15N:14N ratio (δ15N). Rates of nitrogen fixation and methane oxidation were measured using serum bottle incubations performed with Sphagnum collected inside the SPRUCE enclosures in July 2017, 2019, and 2021. Rates were calculated according to the amount of 15N-N2 or 13C-CH4 (methane) incorporated into the Sphagnum tissue or incubation headspace over the 48-hour intion period. In 2017, incubations were performed in growth chambers set to the temperature of the experimental plots during Sphagnum sampling. In 2019 and 2021, the incubations were performed directly inside the experimental plots. The 13C and 15N isotopic natural abundance data and rate measurements presented in this dataset provide insight into the impacts of whole-ecosystem warming and elevated atmospheric carbon dioxide (CO2) on N and C cycling within the Sphagnum phytobiome. This dataset contains two data files in comma-separate values (*.csv) format. Additional metadata are provided: two data dictionaries and a file-level metadata file in comma-separate values (.csv) format and a user guide in PDF (*.pdf) format. Additional datasets on NH4-N availability, porewater concentrations of CH4 and CO2, and Sphagnum groundcover within the SPRUCE experimental plots are available. These are outlined in the Related Datasets section in the user guide.

delta 13 carbon↗
DOE OSTI2021

Thermomechanical Microstructural Predictions of Fracture Nucleation of Zircaloy-4 Alloys With δ and ε Hydride Distributions

Abstract A crystalline dislocation-density formulation that was incorporated with a nonlinear finite-element (FE) method was utilized to understand and to predict the thermomechanical behavior of an hexagonal closest packed (h.c.p.) zircaloy system with hydrides with either face-centered cubic (f.c.c.) or body-centered cubic (b.c.c.) hydrides. This formulation was then used with a recently developed fracture methodology that is adapted for finite inelastic strains and multiphase crystalline systems to understand how different microstructurally based fracture modes nucleate and propagate. The interrelated microstructural characteristics of the different crystalline hydride and matrix phases with the necessary orientation relationships (ORs) have been represented, such that a detailed physical understanding of fracture nucleation and propagation can be predicted for the simultaneous thermomechanical failure modes of hydride populations and the matrix. The effects of volume fraction, morphology, crystalline structure, and orientation and distribution of the hydrides on simultaneous and multiple fracture modes were investigated for radial, circumferential, and mixed distributions. Another key aspect was accounting for temperatures changes due to the effects of thermal conduction and dissipated plastic work and their collective effects on fracture. For hydrided aggregates subjected to high temperatures, thermal softening resulted in higher ductility due to increased dislocation-density activity, which led to higher shear strain accumulation and inhibited crack nucleation and growth. The predictions provide validated insights into why circumferential hydrides are more fracture-resistant than radial hydrides for different volume fractions and thermomechanical loading conditions.

Engineering↗
DOE OSTI2024

OPEN-Augmented Reality GUI for Bioenergy Crop Phenotyping and Precision Agriculture (Donald Danforth Plant Science Center Final Scientific Technical Report)

The project led by the Donald Danforth Plant Science Center, in collaboration with Arizona State University, George Washington University, and Saint Louis University, has made significant strides in advancing the phenotypic analysis of bioenergy crops through the development of an innovative AI processing pipeline. This initiative was primarily funded by ARPA-E, with additional cost-sharing provided by the participating institutions. The project successfully utilized a variety of sensors—3D scanners, thermal, RGB, and hyperspectral—to refine algorithms for data-driven trait signature identification and improve the classification and visualization of plant traits. The developed AI processing pipeline is capable of handling the complex, multidimensional data characteristic of dynamic agricultural environments. 1) Contributions to understanding: The research has advanced the field of plant phenomics by showcasing the synergistic use of various sensor data to enhance the precision of trait analysis in bioenergy crops. Through the integration of 3D scanners, thermal, RGB, and hyperspectral sensors, the project has developed robust data-driven trait signature algorithms and visualization techniques. These innovations have facilitated detailed monitoring and management of plant traits, providing vital insights into plant growth dynamics and stress responses. Further, the project has broadened our understanding of how machine learning can be effectively applied in multi-sensor environments to refine trait analysis. By leveraging diverse datasets, the research has not only improved the accuracy of phenotypic assessments but also established a versatile methodological framework that can be extended beyond agriculture to other fields requiring detailed phenotypic analysis. 2) Technical effectiveness and economic feasibility: The AI processing pipeline developed in this project demonstrated significant technical effectiveness, achieving high throughput analysis of extensive phenotypic data and meeting targeted accuracies. This system exemplified the capability of advanced machine learning technologies to efficiently manage and analyze large, complex datasets. Economically, the implementation of the project-developed pipelines may offer substantial cost savings across multiple sectors. It enhances data analysis processes and significantly reduces the need for manual data interpretation, thereby decreasing both the time and resources required. 3) Public benefit: The project has significantly broadened the scope of agricultural methodologies to enhance phenotypic analysis, with potential applications in various sectors beyond agriculture. Additionally, the initiative fostered an enriching educational and collaborative environment, significantly enhancing the technical skills of participants. It also made substantial contributions to the scientific community by providing open-access data sets and tools, encouraging ongoing research and development across various disciplines. Overall, the project not only met its scientific goals but also showcased the extensive utility of integrating advanced machine learning and sensor data analysis technologies. These advancements have proven instrumental in driving forward both theoretical research and practical applications, setting a strong foundation for future explorations and innovations in data-driven science.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Radiation modeling and experimental validation of sub-cyclotron frequency microwave synchrotron radiation as a diagnostic tool for runaway electrons in present and future tokamaks

The microwave synchrotron radiation in the sub-cyclotron frequency region has been systematically validated as a diagnostic method for runaway electrons (>1 MeV) with radiation modeling and experiments on the DIII-D tokamak. Experimental observations reveal that sub-cyclotron frequency microwave radiation emerges only after runaway electrons are excited during plasma disruptions. Electron gyro-magnetic radiation modeling confirms that only runaway electrons with energies exceeding 1 MeV can emit radiation in the observed sub-cyclotron frequency band on DIII-D. The synchrotron Razin and Beaming effects are modeled in both the sub-cyclotron and standard Electron Cyclotron Emission (ECE) frequency region. Due to the pronounced beaming effect, the modeling supports that a standard ECE system is insensitive to synchrotron radiation from beamed runaway electrons (p ∥ >> p ⟂ ), but very sensitive to ECE radiation from isotropic suprathermal electrons (100 keV or less). DIII-D experiments further show that combining ECE and sub-cyclotron receivers reveals new insights into transient runaway dynamics during the plateau phase. Certain transient crash events appear only in the sub-cyclotron band or lead to opposite changes on the sub-cyclotron and standard ECE signals, supporting models where transient MHD events can exert a different effect on electrons of different energy levels. This novel diagnostic technique offers a powerful tool for studying and detecting runaway electron dynamics in both present-day and high-field future tokamaks. The diagnostic approach has been extrapolated to the SPARC tokamak, where sub-cyclotron radiation is predicted to respond sensitively and solely to runaway electrons during SPARC’s L-mode startup phase.

Yu, Guanying [Hefei Comprehensive National Science↗
DOE OSTI2025

Universal Electronic‐Structure Relationship Governing Intrinsic Magnetic Properties in Permanent Magnets

An electronic-structure-centered perspective is presented on permanent-magnet (PM) design, highlighting two key levers, that is, saturation magnetization (M s ), governed by 3d-band filling and exchange physics, and magnetocrystalline anisotropy energy (MAE), arising from spin-orbit coupling (SOC) on anisotropic orbital populations. Reviewing current practices, including DFT-based MAE/J ij extraction, atomistic-spin and micromagnetic modeling, and high-throughput machine learning (ML) pipelines, three bottlenecks limiting predictive discovery is identified that is i) electronic-structure accuracy for small MAE (sensitive to functional choice, Hubbard U, and many-body effects), ii) finite-temperature and kinetic realism (phonon/magnon renormalization, ordering kinetics), and iii) descriptor and multiscale decoupling (lack of SOC-weighted and orbital-resolved fingerprints). Deep dives into the electronic-structure of Nd─Fe─B and Fe─N show how these fingerprints govern magnetic performance, motivating DFT- and quantum-mechanics-based descriptors for discovery. Unbiased, structure-driven exploration, coupled with high-throughput simulations, ML, generative AI, and reasoning models, accelerates candidate identification and propagates insights across scales. Addressing supply-chain risks, on future needs of designing “critical-element-free” magnets with tailored microstructure and high energy products is emphasized. By integrating electronic fingerprints, AI reasoning, and multiscale modeling, a practical roadmap is provided for rare-earth-lean or rare-earth free, high-performance, sustainable PMs.

Singh, Prashant [Ames Laboratory, and Iowa State U↗
DOE OSTI2024

Cysteine Rich Intestinal Protein 2 is a copper-responsive regulator of skeletal muscle differentiation and metal homeostasis

Copper (Cu) is essential for respiration, neurotransmitter synthesis, oxidative stress response, and transcription regulation, with imbalances leading to neurological, cognitive, and muscular disorders. Here we show the role of a novel Cu-binding protein (Cu-BP) in mammalian transcriptional regulation, specifically on skeletal muscle differentiation using murine primary myoblasts. Utilizing synchrotron X-ray fluorescence-mass spectrometry, we identified murine cysteine-rich intestinal protein 2 (mCrip2) as a key Cu-BP abundant in both nuclear and cytosolic fractions. mCrip2 binds two to four Cu + ions with high affinity and presents limited redox potential. CRISPR/Cas9-mediated deletion of mCrip2 impaired myogenesis, likely due to Cu accumulation in cells. CUT&RUN and transcriptome analyses revealed its association with gene promoters, including MyoD1 and metallothioneins, suggesting a novel Cu-responsive regulatory role for mCrip2. Our work describes the significance of mCrip2 in skeletal muscle differentiation and metal homeostasis, expanding understanding of the Cu-network in myoblasts. Copper (Cu) is essential for various cellular processes, including respiration and stress response, but imbalances can cause serious health issues. This study reveals a new Cu-binding protein (Cu-BP) involved in muscle development in primary myoblasts. Using unbiased metalloproteomic techniques and high throughput sequencing, we identified mCrip2 as a key Cu-BP found in cell nuclei and cytoplasm. mCrip2 binds up to four Cu + ions and has a limited redox potential. Deleting mCrip2 using CRISPR/Cas9 disrupted muscle formation due to Cu accumulation. Further analyses showed that mCrip2 regulates the expression of genes like MyoD1, essential for muscle differentiation, and metallothioneins in response to copper supplementation. This research highlights the importance of mCrip2 in muscle development and metal homeostasis, providing new insights into the Cu-network in cells.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Multi-system analysis of offshore geologic carbon storage: a review of open-source data science solutions

Geologic carbon storage projects are maturing worldwide and the footprint of deployment in the offshore is expanding. At present, there are ten projects in operation or that have been completed, more than 50 in construction and development, and dozens of characterization studies completed or underway. Offshore geologic carbon storage offers potential benefits over onshore geologic carbon storage. These offshore projects are generally remote in location, distant from population centers, and avoid complicated pore space rights while having abundant prospective storage potential. Some offshore fields targeted for carbon storage have comparatively fewer prior borehole penetrations except for areas that have been explored for petroleum production, minimizing potential issues such as pressure interference and infrastructure impacts. Yet offshore geologic carbon storage projects face distinctive technical and economic challenges, such as seafloor geohazards (e.g., seabed instability), expensive maritime transport, and meteorological-oceanographic conditions that can damage infrastructure and impact operations. Analytical capabilities and improved computational speeds have advanced engineering, earth and energy sciences in the wake of the arrival of modern data science over the last decade. These advancements have created an opportunity for integrated, multi-systems modeling approaches utilizing artificial intelligence and machine learning that are no longer limited by computational issues. Analytical tools developed alongside this advancement in data science can be leveraged to calibrate the potential advantages and challenges of carbon storage operations in the offshore. New methods and approaches that incorporate data science to analyze multiple aspects of engineered and natural systems can provide insights that complement the characterization and onsite engineering that traditional commercial and operational software addresses. These new methods and approaches can potentially improve the outcome of energy operations and carbon storage. Providing multi-system, science-driven data analytics enhances the knowledge base that offshore developers, operators, and regulatory bodies may draw from to improve offshore site selection and operational efficiency. Here, we provide a brief synopsis of geologic carbon storage efforts to date, an overview of the engineered and natural systems involved in offshore geologic carbon storage, and a review of publicly available, open-source, offshore and/or carbon storage related data- and science-driven tools developed by 2010 or later that are suitable for screening and assessing regions for offshore geologic carbon storage.

artificial intelligence↗
DOE OSTI2025

Fundamental limit of jet tagging

Identifying the origin of high-energy hadronic jets (jet tagging) has been a critical benchmark problem for machine learning in particle physics. Jets are ubiquitous at colliders and are complex objects that serve as prototypical examples of collections of particles to be categorized. Over the last decade, machine learning-based classifiers have replaced classical observables as the state of the art in jet tagging. Increasingly complex machine learning models are leading to increasingly more effective tagger performance. Our goal is to address the question of convergence—are we getting close to the fundamental limit on jet tagging or is there still potential for computational, statistical, and physical insights for further improvements? We address this question using state-of-the-art generative models to create a realistic, synthetic dataset with a known jet tagging optimum. Various state-of-the-art taggers are deployed on this dataset, showing that there is a significant gap between their performance and the optimum. Our dataset and software are made public to provide a benchmark task for future developments in jet tagging and other areas of particle physics.

Artificial intelligence↗
DOE OSTI2023

SPT-CL J2215−3537: A Massive Starburst at the Center of the Most Distant Relaxed Galaxy Cluster

Abstract We present the discovery of the most distant, dynamically relaxed cool core cluster, SPT-CL J2215−3537 (SPT2215), and its central brightest cluster galaxy (BCG) at z = 1.16. Using new X-ray observations, we demonstrate that SPT2215 harbors a strong cool core with a central cooling time of 200 Myr (at 10 kpc) and a maximal intracluster medium cooling rate of 1900 ± 400 M ⊙ yr −1 . This prodigious cooling may be responsible for fueling the extended, star-forming filaments observed in Hubble Space Telescope imaging. Based on new spectrophotometric data, we detect bright [O ii ] emission in the BCG, implying an unobscured star formation rate (SFR) of 320 − 140 + 230 M ⊙ yr −1 . The detection of a weak radio source (2.0 ± 0.8 mJy at 0.8 GHz) suggests ongoing feedback from an active galactic nucleus (AGN), though the implied jet power is less than half the cooling luminosity of the hot gas, consistent with cooling overpowering heating. The extreme cooling and SFR of SPT2215 are rare among known cool core clusters, and it is even more remarkable that we observe these at such high redshift, when most clusters are still dynamically disturbed. The high mass of this cluster, coupled with the fact that it is dynamically relaxed with a highly isolated BCG, suggests that it is an exceptionally rare system that must have formed very rapidly in the early universe. Combined with the high SFR, SPT2215 may be a high- z analog of the Phoenix cluster, potentially providing insight into the limits of AGN feedback and star formation in the most massive galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Photoresponsive Rotaxanes Switch Lipid Bilayer Neuromorphic Behavior with Light

A rotaxane consisting of a macrocycle ring with two azobenzene units mechanically interlocked onto a bolaamphiphilic axle was incorporated into droplet interface bilayers (DIBs). The azobenzene groups on the ring underwent quasi-reversible, photoisomerization-induced cycling between 1-E and 1-Z configurations when irradiated with 370 and 467 nm light, respectively, enabling programmable access to different history-dependent electrical behaviors from the same membrane. In the 1-E configuration, bilayers exhibited type-IIactive memristance that coincided with increasingly elevated ionic conduction, associated with progressively enhanced bilayer permeability during voltage cycling. In the 1-Z configuration, bilayers displayed type-I, passive memcapacitive behavior, reflecting tighter lipid packing and reduced ionic permeability. Photoswitching also yielded a nonvolatile, photoresponsive memcapacitor that could be modulated repetitively with negligible loss, likely via reversible changes in membrane thickness. Concurrent ohmic leakage currents across the membrane were less than 0.3%. These results agree with previous studies with increasing membrane permeability using photoswitchable rotaxanes and provide new insights into the coupling between volatile and nonvolatile memcapacitance during photoisomerization. More broadly, they demonstrate a new strategy for the manipulation of neuromorphic behaviors in soft materials using light, with implications for brain-inspired computation and sensing.

droplet interface bilayers↗
DOE OSTI2025

Observation of Orbital-Selective Dual Modulations in an Anisotropic Antiferromagnetic Kagome Metal TbTi 3 ⁢Bi 4

Orbital selectivity is pivotal in dictating the phase diagrams of multiorbital systems, with prominent examples including the orbital-selective Mott phase and superconductivity. The intercalation of anisotropic layers represents an effective method for enhancing orbital selectivity and thereby shaping the low-energy physics of multiorbital systems. Despite its potential, related experimental studies, especially those elucidating the correlation between orbital selectivity and magnetism, remain limited. In this work, we systematically examine the interplay between orbital selectivity and magnetism in the newly discovered anisotropic kagome TbTi 3 ⁢Bi 4 single crystal, and report the coexistence of orbital-selective dual-band modulations (𝑞 1 ∼ 1/3⁢𝑎*, 𝑞 2 ∼ 0.28⁢𝑏*) within the antiferromagnetic (AFM) state. By combining soft x-ray and vacuum ultraviolet angle-resolved photoemission spectroscopy measurements, neutron powder diffraction, scanning tunneling microscopy, and density-functional-theory calculations, we identify these dual-band reconstructions as manifestations of the AFM order driven by a (approximately 1/3, 0.28, 0) nesting instability of the intercalated Tb 5⁢𝑑 𝑥⁢𝑧 orbitals. These orbital-selective modulations induce unusual momentum-dependent band folding and lead to the emergence of Dirac cones only at the $\bar{M}$ 1 point, signaling a topological phase transition in the AFM state. Importantly, the discovery of orbital-selective (approximately 1/3, 0.28, 0) AFM order offers crucial insights into the mechanism underlying the fractional magnetization plateau in this kagome AFM metal. Our findings not only underscore the essential role of both conducting and localized electrons in determining the magnetic orders of Ln⁢Ti 3 ⁢Bi 4 (Ln = lanthanide) kagome metals but also offer a pathway for manipulating magnetism through selective control of anisotropic electronic structures.

Zhang, Renjie [Shanghai Jiao Tong University (Chin↗
DOE OSTI2026

Data from "What regulates decomposition in agroecosystems? Insights from reading the tea leaves"

Litter decomposition is a critical Earth process, recycling nutrients and setting a portion of plant tissue on a path toward soil organic matter. Despite this importance, we still lack a good understanding of local factors that regulate decomposition, especially in agroecosystems where management plays an outsized role. To help understand these factors, 1308 tea bags containing green and rooibos tea leaves were buried in 109 plots being exposed to a variety of management practices. This dataset contains the decomposition measurements (mass) of those tea bags that were collected 6 times during the 2018 growing season at 9 long-term experimental farms in Iowa, USA. Additionally, the dataset contains a variety of soil and crop measurements to support the understanding of the soils and the decomposition measurements. Files are presented in .csv format.

Agricultural land management↗
DOE OSTI2026

Organic Cation Influence on Organic–Inorganic Thermal Equilibration within 2D Metal Halide Perovskites

Two-dimensional inorganic–organic lead halide perovskites exhibit tunable optoelectronic properties that are dictated by alternating layers of metal halide octahedra and organic cations. In such hybrid materials, vibrational coupling and thermalization between the low mass, insulating, organic cation spacers, and high mass inorganic octahedra remains poorly understood. Here, in this study, using femtosecond infrared-pump electronic-probe (IPEP) spectroscopy, we investigate the role of organic cation identity regarding the kinetics of vibrational energy exchange and sublattice mechanical coupling. Linear aliphatic cation-containing 2D perovskites with 4, 6, or 8 carbons (butylammonium = BA, hexylammonium = HA, and octylammonium = OA) were produced as thin films and mid-infrared pump pulses and then selectively excited organic cation stretch vibrations. Thermal energy introduction was evaluated via visible-wavelength optical probing that conveys inorganic octahedra response, owing to changes in electronic absorption. Multiple distinct spectral shifts appear upon excitation of the organic cations. Following an initial optical Stark shift caused by pump–probe temporal overlap, a redshift occurs within the first 10 ps that we attribute to compression of the octahedra by the expanded organic layers. Subsequently, a blueshift of the bandgap occurs commensurate with equilibration of both sublattices at an elevated temperature with a time constant of 21.8 to 31.6 ps depending on the spacer identity, with thermal energy transfer slowing by nearly 50% for the longest linker. By examining the effect that altered cation identity has on vibrational energy exchange, this work begins to offer routes to tune nonequilibrium response as well as provides insight into the fundamental impacts of organic spacers on thermal energy exchange in perovskite materials.

Peifer, Shoshanna E. [Northwestern Univ., Evanston↗
DOE OSTI2025

Nonadiabatic dynamics of photoexcited thiopyridone isomers: An interplay between El-Sayed’s conditions and energy gap law

We investigated the non-adiabatic dynamics of photoexcited thiopyridone systems across their ortho-, meta-, and para-isomeric forms. The relaxation pathways of the three isomers in both gas phase and solvent environments are mapped using surface hopping dynamics based on time-dependent density functional theory. Our analysis highlights the influence of isomeric structures on photophysical behavior, offering insights into design principles to control photochemical phenomena. The simulations suggest a systematic reduction in the rate of intersystem crossing (ISC) from ortho- to meta- to para-isomer. Comparisons with multiconfigurational wave function methods in the gas phase further demonstrate how electronic structure influences the predicted dynamical pathways. The simulated dynamics demonstrates that the spin–orbit coupling strength alone does not determine the rate of ISC, as both state energetics and underlying electronic and structural features play decisive roles. These aspects explain the much slower ISC in the para-isomer, as well as the non-negligible role of the El-Sayed forbidden pathway in the computed ISC dynamics.

Complete-active space self-consistent field↗
DOE OSTI2026

HPC ODA Commons [SWR-26-003]

HPC ODA Commons is a community-driven platform for standardizing HPC operational data analytics. HPC sites generate enormous volumes of operational data - scheduler logs, accounting records, monitoring streams - but turning that data into actionable insight is needlessly hard. Each site builds bespoke parsers, schemas, and evaluation pipelines. Results can't be compared across institutions. Promising analytics ideas stay siloed because there's no shared language for describing the data, the experiments, or the outcomes. HPC ODA Commons fixes this by establishing community-governed contracts - versioned schemas, canonical artifacts, and benchmark recipes - that make ODA workflows discoverable, reproducible, and comparable. It pairs these standards with a practical, CLI-first toolkit that lets operators and researchers go from raw logs to standardized results without sending data off-cluster.

Menear, Kevin [National Laboratory of the Rockies ↗
DOE OSTI2021

Determination of Thermal Expansion, Defect Formation Energy, and Defect-Induced Strain of α-U Via ab Initio Molecular Dynamics

Uranium (U) is often alloyed with molybdenum (Mo) or zirconium (Zr) in order to stabilize its high-temperature body-centered cubic phase for use in nuclear reactors. However, in all metallic fuel forms, the α phase of U remains in some fraction. This phase decomposition due to temperature or compositional variance can play an outsized role on fuel performance and microstructural evolution. Relatively little is known about fundamental point defect properties in α -U at non-zero temperatures, from either computational or experimental studies. This work performs the first thorough evaluation of the α phase of U via ab initio molecular dynamics (AIMD). A number of thermophysical properties are calculated as a function of temperature, including equilibrium lattice parameters, thermal expansion, and heat capacity. These results indicate a two-region behavior, with the transition at 400 K. The thermal expansion/contraction in the a / b direction occurs rapidly from 100 up to 400 K, after which a more linear and gradual change in the lattice constant takes place. The volumetric expansion matches experiments quantitatively, but the individual lattice constant expansion only matches experiments qualitatively. Point defect formation energies and induced lattice strains are also determined as a function of temperature, providing insight on defect populations and the fundamentals of irradiation growth in α -U. Interstitials induce significantly more strain than vacancies, and the nature of that strain is highly dependent on the individual lattice directions. The direction of point defect-induced lattice strain is contrary to the irradiation growth behavior of α -U. This work shows that isolated point defects cannot be the primary driving force responsible for the significant irradiation-induced growth of α -U observed experimentally.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗